Abstract: Human motion prediction refers to predicting future motion sequences based on given historical motion sequences, which can provide pre-judgment basis for applications such as intelligent monitoring and human-computer interaction. However, the randomness and uncertainty of human motions make motion prediction extremely difficult. In response to the problems that most current motion prediction methods have insufficient diversity or that the prediction results deviate from the reasonable range of human motions, a random human motion prediction method based on denoising diffusion probabilistic model is proposed. Firstly, a spatio-temporal Transformer denoising diffusion prediction network is constructed, in which the spatial Transformer module is used to encode joint embeddings to capture the local relationships among 3D joints within a single frame, and the temporal Transformer module captures the global dependencies across frames to improve the consistency between the predicted motion and the historical motion sequences. Then, the prediction action sequence refinement module is designed, and the GCN-AT residual module is introduced into the discrete cosine transform space to refine the prediction results, generate a more natural and smooth action sequence, improve the accuracy of prediction, and solve the problem of stuttering and incoherence of prediction actions. The experimental results on the benchmark dataset indicate that the proposed method outperforms the comparison methods in terms of accuracy and fidelity, and achieves the optimal values in the FDE and MMADE indicators.